Instructions to use google/siglip-base-patch16-256-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip-base-patch16-256-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip-base-patch16-256-multilingual") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-256-multilingual") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip-base-patch16-256-multilingual") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fba5d6b00a3b9d343cd0934d6c8d1f22666e4c8208a570cc9ea4ba9a6d94e5cf
- Size of remote file:
- 16.4 MB
- SHA256:
- babbf9fed5f505ac6b28fd820b5108ca6882332ce6b921c8d33f8ff69bf841d9
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